Exploring Magic Interactions for Collaboration in Virtual Reality Learning Factory
Bibliographic record
Abstract
A hands-on curriculum that blends theory and practical skills is essential to teach manufacturing.An integral part of such a curriculum is a learning factory, which allows engineering students to experience the entire manufacturing cycle of a product in a realistic factory environment.In addition to learning the required technical skills, students can practice their collaborative skills and communication via teamwork in a learning factory.With virtual reality (VR), environments can be made using game engines that simulate their real-world equivalents, providing realistic experiences.Compared to traditional remote learning, VR-based learning together with online remote learning is experiential, allows for natural interaction, and is only limited by the capabilities of the hardware running the virtual environments.The cost of VR devices has dramatically reduced with standalone VR devices such as Meta Quest 2, making these devices a compelling option for specialized educational simulations.A VR Learning Factory should support synchronous collaboration of multiple learners in the same environment.This is a critical advantage of using VR, since collaboration is an essential skill for engineers.To maximize this benefit, it is imperative to develop an appropriate VR interaction mode, because it can greatly influence the effectiveness of collaborations.In this research, we explore multi-user interaction within the context of the VR Learning Factory and compare two modes of virtual user interaction that we call natural and magic.Magic interactions include three additional tools: object container, holographic representation, and multi-object selection.We conduct an analysis of the two modes of VR interaction in a craft production task and show increased performance of using magic interactions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".